Generic habitat suitability criteria (HC) are often developed from spatially and temporally variable hydroecological datasets to increase generality, cost-effectiveness, and time-efficiency of habitat models. For benthic macroinvertebrates (BMIs), however, there is no prior knowledge on the spatiotemporal variation in their habitat preferences and how this may be reflected in the final environmental flow (e-flow) predictions. In this study, we used a large, spatiotemporally variable BMI-hydroecological dataset and developed generic, local, and season-specific subsets of HC for three seasons and two river types within various data pre-treatment options. Each subset was used to train a fuzzy habitat model, predict the habitat suitability in two hydrodynamically-simulated river reaches, and develop/compare model-based e-flow scenarios. We found that BMIs shift their habitat preferences among seasons and river types; consequently, spatiotemporally variable e-flow predictions were developed, with the seasonal variation being greater than the typological one. Within this variation, however, we found that with proper data pre-treatment, the minimum-acceptable e-flows from the generic models mostly (65–90%) lay within the acceptable e-flows predicted by the local and season-specific models. We conclude that, within specific limitations, generic BMI-HC can be used for geographically extended, cost-effective e-flow assessments, compensating for the within-limits loss of predictive accuracy.     «

Generic habitat suitability criteria (HC) are often developed from spatially and temporally variable hydroecological datasets to increase generality, cost-effectiveness, and time-efficiency of habitat models. For benthic macroinvertebrates (BMIs), however, there is no prior knowledge on the spatiotemporal variation in their habitat preferences and how this may be reflected in the final environmental flow (e-flow) predictions. In this study, we used a large, spatiotemporally variable BMI-hyd...     »